Anaerobic CUE
05 Differential abundance modelling
Roey Angel
2021-02-09
Differential abundance modelling of SIP gradients
Here we attempt to detect ASVs that were labelled with 13C in our soil incubations using differential abundance modelling. Using DESeq2 \citep(love_moderated_2014) we compare the relative abundance of each ASV in the fractions where ^13^C label is supposed to be found ()
Setting general parameters:
set.seed(2021)
alpha_thresh <- 0.05
LFC_thresh <- 0.26
samples_prep_path <- "./"
data_path <- "./DADA2_pseudo/"
# Metadata_table <- "./AnCUE_Metadata_decontam.csv"
# Seq_table <- "DADA2.seqtab_nochim_decontam.tsv"
# Seq_file <- "DADA2.Seqs_decontam.fa"
Ps_file <- "Ps_obj_decontam_filt3.Rds"
Tree_file <- "./Tree/DADA2.Seqs_decontam_filtered.filtered.align.treefile"Read phyloseq object
Beta diversity analysis
Let us look first at
Ord <- ordinate(Ps_obj_SIP, "CAP", "horn", formula = ~ Oxygen + Density.zone)
explained <- as.numeric(format(round(eigenvals(Ord)/sum(eigenvals(Ord)) * 100, 1), nsmall = 1))
Ord_plt <- plot_ordination(Ps_obj_SIP, Ord, type = "samples", color = "Label..13C.", justDF = TRUE)
p_ord_joint <- ggplot(Ord_plt) +
geom_point(aes(
x = CAP1,
y = CAP2,
color = Label..13C.,
size = Density..g.ml.1.,
shape = Oxygen
), alpha = 2 / 3) +
guides(colour = guide_legend(title = "Labelling"),
size = guide_legend(title = "Density (g ml<sup>-1</sup>)"),
shape = guide_legend(title = "Oxygen")) +
ggsci::scale_colour_locuszoom() +
# scale_colour_manual(values = Gradient.colours) +
# scale_fill_manual(values = Gradient.colours, guide = "none") +
labs(x = sprintf("CAP1 (%s%%)", explained[1]),
y = sprintf("CAP2 (%s%%)", explained[2])) +
coord_fixed(ratio = sqrt(explained[2] / explained[1])) +
theme(legend.justification = "top",
legend.title = element_markdown(size = 11)
) +
scale_size_continuous(breaks = c(seq(min(Ord_plt$Density..g.ml.1.),
max(Ord_plt$Density..g.ml.1.),
length.out = 5),
1),
range = c(0.1, 5)) +
facet_grid(Site ~ Hours) +
ggtitle("Joint analysis")
print(p_ord_joint)
test_expr_1 <- "(Site == '${Site}' & Oxygen == '${Oxygen}' & Label..13C. == 'Unlabelled') | (Site == '${Site}' & Oxygen == '${Oxygen}' & Label..13C. == '${Label..13C.}')"
params_1 <- get_treatment_params(Ps_obj_SIP, c("Site",
"Oxygen",
"Glucose",
"Label..13C."),
"Label..13C. != 'Unlabelled'")
Ps_obj_SIP_noTime_l <- phyloseq_subset(Ps_obj_SIP, params_1, test_expr_1)
# names(Ps_obj_SIP_noTime_l) %<>%
# map(., ~str_remove_all(.x, "\\s\\|\\s.*")) %>%
# map(., ~str_remove_all(.x, "\\(|\\)|Site == |Hours == |Oxygen == |Label..13C. == |'")) %>%
# map(., ~str_replace_all(.x, "([0-9]+)", "\\1 h"))
test_expr_2 <- "(Site == '${Site}' & Hours == '${Hours}' & Oxygen == '${Oxygen}' & Label..13C. == '${Label..13C.}') | (Site == '${Site}' & Hours == '${Hours}' & Oxygen == '${Oxygen}' & Label..13C. == '${Label..13C.}')"
params_2 <- get_treatment_params(Ps_obj_SIP, c("Site",
"Hours",
"Oxygen",
"Glucose",
"Label..13C."))
# Generate a list of subsetted phyloseq objects
Ps_obj_SIP_byTime_l <- phyloseq_subset(Ps_obj_SIP, params_2, test_expr_2)
names(Ps_obj_SIP_byTime_l) %<>%
map(., ~str_remove_all(.x, "\\s\\|\\s.*")) %>%
map(., ~str_remove_all(.x, "\\(|\\)|Site == |Hours == |Oxygen == |Label..13C. == |'")) %>%
map(., ~str_replace_all(.x, "([0-9]+)", "\\1 h"))
test_expr_3 <- "(Site == '${Site}' & Oxygen == '${Oxygen}' & Label..13C. == '${Label..13C.}') | (Site == '${Site}' & Oxygen == '${Oxygen}' & Label..13C. == '${Label..13C.}')"
params_3 <- get_treatment_params(Ps_obj_SIP, c("Site",
"Oxygen",
"Glucose",
"Label..13C."))
# Generate a list of subsetted phyloseq objects
Ps_obj_SIP_noTime_split_l <- phyloseq_subset(Ps_obj_SIP, params_3, test_expr_3)
names(Ps_obj_SIP_noTime_split_l) %<>%
map(., ~str_remove_all(.x, "\\s\\|\\s.*")) %>%
map(., ~str_remove_all(.x, "\\(|\\)|Site == |Hours == |Oxygen == |Label..13C. == |'")) %>%
map(., ~str_replace_all(.x, "([0-9]+)", "\\1 h")) Ord_l <- mclapply(Ps_obj_SIP_byTime_l, function(x) {ordinate(x, "CAP", "horn", formula = ~ Oxygen + Density.zone)}, mc.cores = nrow(params_2))
explained <- mclapply(Ord_l, function(x) {as.numeric(format(round(eigenvals(x)/sum( eigenvals(x)) * 100, 1), nsmall = 1))}, mc.cores = nrow(params_2))
bind_rows(map(seq(1, length(Ps_obj_SIP_byTime_l)), ~plot_ordination(Ps_obj_SIP_byTime_l[[.x]], Ord_l[[.x]], type = "samples", color = "Label..13C.", justDF = TRUE))) ->
# mutate(MDS1 = coalesce(MDS1, MDS2)) ->
Ord_plt_time_df
p_ord_split_time <- ggplot(Ord_plt_time_df) +
geom_point(aes(
x = CAP1,
y = MDS1,
color = Label..13C.,
size = Density..g.ml.1.,
shape = Oxygen
), alpha = 2 / 3) +
# guides(colour = guide_legend(title = "Labelling"), shape = guide_legend(title = "Oxygen")) +
ggsci::scale_colour_locuszoom() +
# labs(x = sprintf("CAP1 (%s%%)", explained[1]),
# y = sprintf("CAP2 (%s%%)", explained[2])) +
# coord_fixed(ratio = sqrt(explained[2] / explained[1])) +
theme(legend.justification = "top") +
facet_grid(Site ~ Hours) +
ggtitle("Split analysis (incl. time)")
print(p_ord_split_time)
Ord_l <- mclapply(Ps_obj_SIP_noTime_l, function(x) {ordinate(x, "CAP", "horn", formula = ~ Oxygen + Density.zone)}, mc.cores = nrow(params_1))
explained <- mclapply(Ord_l, function(x) {as.numeric(format(round(eigenvals(x)/sum( eigenvals(x)) * 100, 1), nsmall = 1))}, mc.cores = nrow(params_1))
bind_rows(map(seq(1, length(Ps_obj_SIP_noTime_l)), ~plot_ordination(Ps_obj_SIP_noTime_l[[.x]], Ord_l[[.x]], type = "samples", color = "Label..13C.", justDF = TRUE))) ->
# mutate(MDS1 = coalesce(MDS1, MDS2)) ->
Ord_plt_df
p_ord_split_time <- ggplot(Ord_plt_time_df) +
geom_point(aes(
x = CAP1,
y = MDS1,
color = Label..13C.,
size = Density..g.ml.1.,
shape = Oxygen
), alpha = 2 / 3) +
# guides(colour = guide_legend(title = "Labelling"), shape = guide_legend(title = "Oxygen")) +
ggsci::scale_colour_locuszoom() +
# labs(x = sprintf("CAP1 (%s%%)", explained[1]),
# y = sprintf("CAP2 (%s%%)", explained[2])) +
# coord_fixed(ratio = sqrt(explained[2] / explained[1])) +
theme(legend.justification = "top") +
facet_grid(Site ~ Oxygen) +
ggtitle("Split analysis (excl. time)")
print(p_ord_split_time)
# generate a deseq object
DESeq_obj_SIP_byTime_l <- mclapply(Ps_obj_SIP_byTime_l,
function(x) {phyloseq_to_deseq2_safe(x,
test_condition = "Density.zone",
ref_level = "Light")},
mc.cores = nrow(params_2))
# run dds pipeline
DESeq_obj_SIP_byTime_l %<>% mclapply(.,
function(x) {DESeq(x,
test = "Wald",
fitType = "local")},
mc.cores = nrow(params_2)) # run dds pipeline
# extract results from a DESeq analysis
DESeq_res_SIP_byTime_l <- mclapply(DESeq_obj_SIP_byTime_l,
function(x) {
results(x,
altHypothesis = "greater",
alpha = alpha_thresh,
contrast = c("Density.zone", "Heavy", "Light"))}, # redundant if phyloseq_to_deseq2_safe() was used but doesn't hurt
mc.cores = nrow(params_2))
DESeq_res_SIP_byTime_LFC_l <- mclapply(DESeq_obj_SIP_byTime_l,
function(x) {
results(x,
lfcThreshold = LFC_thresh,
altHypothesis = "greater",
alpha = alpha_thresh,
contrast = c("Density.zone", "Heavy", "Light"))}, # redundant if phyloseq_to_deseq2_safe() was used but doesn't hurt
mc.cores = nrow(params_2)) # Extract results from a DESeq analysis
DESeq_res_SIP_byTime_LFC_shrink_l <- map(seq(length(DESeq_obj_SIP_byTime_l)),
~lfcShrink(DESeq_obj_SIP_byTime_l[[.x]],
res = DESeq_res_SIP_byTime_LFC_l[[.x]],
coef = "Density.zone_Heavy_vs_Light",
type = "ashr"))
names(DESeq_res_SIP_byTime_LFC_shrink_l) <- names(DESeq_res_SIP_byTime_LFC_l)
# Compare
plotMA(DESeq_res_SIP_byTime_l[[2]], ylim = c(-2,2))
# summarise results (lfcShrink doesn't change the values)
# map2(DESeq_res_SIP_byTime_l, print(names(DESeq_res_SIP_byTime_l)), ~summary(.x)) # summarise results
for (i in seq(1, length(DESeq_res_SIP_byTime_l))) { # didn't manage with map
print(names(DESeq_res_SIP_byTime_l[i]))
summary(DESeq_res_SIP_byTime_l[[i]])
}## [1] "Certovo & 12 h & Anoxic & Labelled"
##
## out of 3026 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 19, 0.63%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 3, 0.099%
## low counts [2] : 2846, 94%
## (mean count < 41)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 216 h & Anoxic & Labelled"
##
## out of 2878 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 127, 4.4%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1374, 48%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 24 h & Anoxic & Labelled"
##
## out of 2755 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 3, 0.11%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 4, 0.15%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 48 h & Anoxic & Labelled"
##
## out of 2787 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 31, 1.1%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1541, 55%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 216 h & Anoxic & Unlabelled"
##
## out of 2825 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 0, 0%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 5, 0.18%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 12 h & Oxic & Labelled"
##
## out of 3053 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 14, 0.46%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 2408, 79%
## (mean count < 5)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 24 h & Oxic & Labelled"
##
## out of 2954 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 167, 5.7%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1188, 40%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 48 h & Oxic & Labelled"
##
## out of 2882 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 160, 5.6%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 992, 34%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 72 h & Oxic & Labelled"
##
## out of 2898 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 198, 6.8%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 1, 0.035%
## low counts [2] : 1274, 44%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 72 h & Oxic & Unlabelled"
##
## out of 2979 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 6, 0.2%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 6, 0.2%
## low counts [2] : 0, 0%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 12 h & Anoxic & Labelled"
##
## out of 3031 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 104, 3.4%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1562, 52%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 216 h & Anoxic & Labelled"
##
## out of 3210 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 33, 1%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 116, 3.6%
## low counts [2] : 2445, 76%
## (mean count < 11)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 24 h & Anoxic & Labelled"
##
## out of 2848 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 69, 2.4%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1362, 48%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 48 h & Anoxic & Labelled"
##
## out of 2758 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 13, 0.47%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 53, 1.9%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 216 h & Anoxic & Unlabelled"
##
## out of 2862 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 1, 0.035%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 2, 0.07%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 12 h & Oxic & Labelled"
##
## out of 2923 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 81, 2.8%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 1, 0.034%
## low counts [2] : 1846, 63%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 24 h & Oxic & Labelled"
##
## out of 2548 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 137, 5.4%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1206, 47%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 48 h & Oxic & Labelled"
##
## out of 2531 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 27, 1.1%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1485, 59%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 72 h & Oxic & Labelled"
##
## out of 2556 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 158, 6.2%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1307, 51%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 72 h & Oxic & Unlabelled"
##
## out of 2798 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 20, 0.71%
## LFC < 0 (down) : 0, 0%
## outliers [1] : 7, 0.25%
## low counts [2] : 1546, 55%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsfor (i in seq(1, length(DESeq_res_SIP_byTime_LFC_l))) { # didn't manage with map
print(names(DESeq_res_SIP_byTime_LFC_l[i]))
summary(DESeq_res_SIP_byTime_LFC_l[[i]])
}## [1] "Certovo & 12 h & Anoxic & Labelled"
##
## out of 3026 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 3, 0.099%
## low counts [2] : 7, 0.23%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 216 h & Anoxic & Labelled"
##
## out of 2878 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 37, 1.3%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1484, 52%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 24 h & Anoxic & Labelled"
##
## out of 2755 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 1, 0.036%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 4, 0.15%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 48 h & Anoxic & Labelled"
##
## out of 2787 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 15, 0.54%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1222, 44%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 216 h & Anoxic & Unlabelled"
##
## out of 2825 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 5, 0.18%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 12 h & Oxic & Labelled"
##
## out of 3053 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 7, 0.23%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 8, 0.26%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 24 h & Oxic & Labelled"
##
## out of 2954 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 107, 3.6%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 905, 31%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 48 h & Oxic & Labelled"
##
## out of 2882 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 122, 4.2%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 331, 11%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 72 h & Oxic & Labelled"
##
## out of 2898 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 142, 4.9%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 1, 0.035%
## low counts [2] : 1329, 46%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 72 h & Oxic & Unlabelled"
##
## out of 2979 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 6, 0.2%
## low counts [2] : 0, 0%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 12 h & Anoxic & Labelled"
##
## out of 3031 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 27, 0.89%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1678, 55%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 216 h & Anoxic & Labelled"
##
## out of 3210 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 116, 3.6%
## low counts [2] : 3, 0.093%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 24 h & Anoxic & Labelled"
##
## out of 2848 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 22, 0.77%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 2340, 82%
## (mean count < 15)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 48 h & Anoxic & Labelled"
##
## out of 2758 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 12, 0.44%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1527, 55%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 216 h & Anoxic & Unlabelled"
##
## out of 2862 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 2, 0.07%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 12 h & Oxic & Labelled"
##
## out of 2923 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 57, 2%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 1, 0.034%
## low counts [2] : 1790, 61%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 24 h & Oxic & Labelled"
##
## out of 2548 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 101, 4%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1061, 42%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 48 h & Oxic & Labelled"
##
## out of 2531 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 13, 0.51%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1820, 72%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 72 h & Oxic & Labelled"
##
## out of 2556 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 115, 4.5%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1113, 44%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 72 h & Oxic & Unlabelled"
##
## out of 2798 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 3, 0.11%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 7, 0.25%
## low counts [2] : 0, 0%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results# Store labelled OTUs and save them to a file
# DESeq_res_SIP_byTime_l %>%
# map(., ~subset(.x, padj < alpha_thresh & log2FoldChange > LFC_thresh)) %>%
# map(., ~as.data.frame(.x)) %>%
# map(., ~rownames_to_column(.x, "ASV")) %>%
# bind_rows(., .id = "Comparison") %>%
# arrange(Comparison, desc(baseMean)) %T>%
# write_csv(., file = "DESeq2_byTime_a-0.05.txt") ->
# DESeq_res_SIP_byTime_df
DESeq_res_SIP_byTime_LFC_l %>%
map(., ~subset(.x, padj < alpha_thresh & log2FoldChange > LFC_thresh)) %>%
map(., ~as.data.frame(.x)) %>%
map(., ~rownames_to_column(.x, "ASV")) %>%
bind_rows(., .id = "Comparison") %>%
arrange(Comparison, desc(baseMean)) %>%
separate(., "Comparison" ,c("Site","Hours", "Oxygen", "Label"), sep = " & ") %T>%
write_csv(., file = "DESeq2_byTime_a-0.05_LFC0-322.txt") ->
DESeq_res_SIP_byTime_LFC_sig_df
# ps_obj <- Ps_obj_SIP
# DESeq_results <- DESeq_res_SIP_byTime_LFC0.322_l[9]
# plot_DESeq(DESeq_results, ps_obj, plot_title = names(DESeq_results))
DESeq_plots <- lapply(seq(length(DESeq_res_SIP_byTime_LFC_shrink_l)),
function(x) {plot_DESeq(DESeq_res_SIP_byTime_LFC_shrink_l[x],
Ps_obj_SIP, plot_title = names(DESeq_res_SIP_byTime_LFC_shrink_l[x]))})
(DESeq_plots[[6]] +
theme(legend.position = "none") +
theme(axis.text.x = element_blank())) +
(DESeq_plots[[1]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[7]] +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[3]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[8]] +
theme(legend.position = "none") +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[4]] +
theme(legend.position = "none") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank()) +
ylim(NA, 5)) +
(DESeq_plots[[9]] +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[2]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[10]] +
theme(legend.position = "none")) +
(DESeq_plots[[5]] +
theme(legend.position = "none",
axis.title.y = element_blank())) +
plot_layout(ncol = 2, guides = "collect") &
theme(legend.position = 'bottom')
(DESeq_plots[[16]] +
theme(legend.position = "none") +
theme(axis.text.x = element_blank())) +
(DESeq_plots[[11]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[17]] +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[13]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[18]] +
theme(legend.position = "none") +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[14]] +
theme(legend.position = "none") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[19]] +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[12]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank()) +
ylim(-3, NA)) +
(DESeq_plots[[20]] +
theme(legend.position = "none")) +
(DESeq_plots[[15]] +
theme(legend.position = "none",
axis.title.y = element_blank())) +
plot_layout(ncol = 2, guides = "collect") &
theme(legend.position = 'bottom')
plot_otus_by_density(ps_obj = Ps_obj_SIP_noTime_l[[2]],
ASV2plot = filter(DESeq_res_SIP_byTime_LFC_sig_df, Site == "Certovo", Oxygen == "Oxic"))
plot_otus_by_density(ps_obj = Ps_obj_SIP_noTime_l[[1]],
ASV2plot = filter(DESeq_res_SIP_byTime_LFC_sig_df, Site == "Certovo", Oxygen == "Anoxic"))
plot_otus_by_density(ps_obj = Ps_obj_SIP_noTime_l[[4]],
ASV2plot = filter(DESeq_res_SIP_byTime_LFC_sig_df, Site == "Plesne", Oxygen == "Oxic"))
plot_otus_by_density(ps_obj = Ps_obj_SIP_noTime_l[[3]],
ASV2plot = filter(DESeq_res_SIP_byTime_LFC_sig_df, Site == "Plesne", Oxygen == "Anoxic"))
c("Certovo Oxic 12 h", "Certovo Oxic 24 h", "Certovo Oxic 48 h", "Certovo Oxic 72 h", "Certovo Anoxic 12 h", "Certovo Anoxic 24 h", "Certovo Anoxic 48 h", "Certovo Anoxic 216 h", "Plesne Oxic 12 h", "Plesne Oxic 24 h", "Plesne Oxic 48 h", "Plesne Oxic 72 h", "Plesne Anoxic 12 h", "Plesne Anoxic 24 h", "Plesne Anoxic 48 h", "Plesne Anoxic 216 h" ) ->
col_order
DESeq_res_SIP_byTime_LFC_l %>%
map(., ~as.data.frame(.x)) %>%
map(., ~rownames_to_column(.x, "ASV")) %>%
bind_rows(., .id = "Comparison") %>%
filter(str_detect(Comparison, "Labelled")) %>% # remove unlabelled samples [c(-5, -10, -15, -20)]
mutate(Labelled = ifelse(padj < alpha_thresh & log2FoldChange > LFC_thresh, "Labelled", "Unlabelled")) %>%
# arrange(Comparison, desc(baseMean)) %>%
separate(., "Comparison" ,c("Site","Hours", "Oxygen", "Label"), sep = " & ") %>%
mutate(Site_Oxygen_Hours = paste(Site, Oxygen, Hours)) %>%
mutate(across(Site_Oxygen_Hours, ~factor(., levels = col_order))) ->
# mutate(Site_oxygen = paste(Site, Oxygen)) ->
DESeq_res_SIP_byTime_all_df
# Summarise number of labelled and unlabelled ASVs
DESeq_res_SIP_byTime_all_df %>%
group_by(Labelled) %>%
summarise(n = n()) | Labelled | n |
|---|---|
| Labelled | 778 |
| Unlabelled | 37162 |
| NA | 21148 |
# detect taxa with NA from DESeq analysis
DESeq_res_SIP_byTime_all_df %<>%
filter(!is.na(Labelled)) #%>%
# pull(Labelled) ->
# bad_seqs
# remove NA taxa from PS obj
Ps_obj_SIP %>%
prune_taxa(DESeq_res_SIP_byTime_all_df$ASV, .) ->
Ps_obj_SIP4tree_plot
p_actino_t <- plot_ggtree(Ps_obj_SIP4tree_plot,
rank = "Class",
subrank = "Order",
Taxa2plot = "Actinobacteria")
p_actino_HM <- plot_ggtree_heatmap(p_actino_t,
Ps_obj_SIP4tree_plot,
DESeq_res_SIP_byTime_all_df,
rank = "Class",
Taxa2plot = "Actinobacteria",
sample_names = "Site_Oxygen_Hours",
sample_colours = rep(brewer.pal(n = 11,
"RdYlGn")[c(1, 3, 11, 9)],
each = 4))
p_actino_t + p_actino_HM
p_Aproteo_t <- plot_ggtree(Ps_obj_SIP4tree_plot,
rank = "Class",
subrank = "Order",
Taxa2plot = "Alphaproteobacteria")
p_Aproteo_HM <- plot_ggtree_heatmap(p_Aproteo_t,
Ps_obj_SIP4tree_plot,
DESeq_res_SIP_byTime_all_df,
rank = "Class",
Taxa2plot = "Alphaproteobacteria",
sample_names = "Site_Oxygen_Hours",
sample_colours = rep(brewer.pal(n = 11,
"RdYlGn")[c(1, 3, 11, 9)],
each = 4))
p_Aproteo_t + p_Aproteo_HM
p_Gproteo_t <- plot_ggtree(Ps_obj_SIP4tree_plot,
rank = "Class",
subrank = "Order",
Taxa2plot = "Gammaproteobacteria")
p_Gproteo_HM <- plot_ggtree_heatmap(p_Gproteo_t,
Ps_obj_SIP4tree_plot,
DESeq_res_SIP_byTime_all_df,
rank = "Class",
Taxa2plot = "Gammaproteobacteria",
sample_names = "Site_Oxygen_Hours",
sample_colours = rep(brewer.pal(n = 11,
"RdYlGn")[c(1, 3, 11, 9)],
each = 4))
p_Gproteo_t + p_Gproteo_HM
p_acido_t <- plot_ggtree(Ps_obj_SIP4tree_plot,
rank = "Phylum",
subrank = "Class",
Taxa2plot = "Acidobacteriota")
p_acido_HM <- plot_ggtree_heatmap(p_acido_t,
Ps_obj_SIP4tree_plot,
DESeq_res_SIP_byTime_all_df,
rank = "Phylum",
Taxa2plot = "Acidobacteriota",
sample_names = "Site_Oxygen_Hours",
sample_colours = rep(brewer.pal(n = 11,
"RdYlGn")[c(1, 3, 11, 9)],
each = 4))
p_acido_t + p_acido_HM
p_verruc_t <- plot_ggtree(Ps_obj_SIP4tree_plot,
rank = "Phylum",
subrank = "Class",
Taxa2plot = "Verrucomicrobiota")
p_verruc_HM <- plot_ggtree_heatmap(p_verruc_t,
Ps_obj_SIP4tree_plot,
DESeq_res_SIP_byTime_all_df,
rank = "Phylum",
Taxa2plot = "Verrucomicrobiota",
sample_names = "Site_Oxygen_Hours",
sample_colours = rep(brewer.pal(n = 11,
"RdYlGn")[c(1, 3, 11, 9)],
each = 4))
p_verruc_t + p_verruc_HM
p_bacteroi_t <- plot_ggtree(Ps_obj_SIP4tree_plot,
rank = "Phylum",
subrank = "Class",
Taxa2plot = "Bacteroidota")
p_bacteroi_HM <- plot_ggtree_heatmap(p_bacteroi_t,
Ps_obj_SIP4tree_plot,
DESeq_res_SIP_byTime_all_df,
rank = "Phylum",
Taxa2plot = "Bacteroidota",
sample_names = "Site_Oxygen_Hours",
sample_colours = rep(brewer.pal(n = 11,
"RdYlGn")[c(1, 3, 11, 9)],
each = 4))
p_bacteroi_t + p_bacteroi_HM
p_firmi_t <- plot_ggtree(Ps_obj_SIP4tree_plot,
rank = "Phylum",
subrank = "Class",
Taxa2plot = "Firmicutes")
p_firmi_HM <- plot_ggtree_heatmap(p_firmi_t,
Ps_obj_SIP4tree_plot,
DESeq_res_SIP_byTime_all_df,
rank = "Phylum",
Taxa2plot = "Firmicutes",
sample_names = "Site_Oxygen_Hours",
sample_colours = rep(brewer.pal(n = 11,
"RdYlGn")[c(1, 3, 11, 9)],
each = 4))
p_firmi_t + p_firmi_HM
c("Certovo Oxic 12 h", "Certovo Oxic 24 h", "Certovo Oxic 48 h", "Certovo Oxic 72 h", "Certovo Anoxic 12 h", "Certovo Anoxic 24 h", "Certovo Anoxic 48 h", "Certovo Anoxic 216 h", "Plesne Oxic 12 h", "Plesne Oxic 24 h", "Plesne Oxic 48 h", "Plesne Oxic 72 h", "Plesne Anoxic 12 h", "Plesne Anoxic 24 h", "Plesne Anoxic 48 h", "Plesne Anoxic 216 h" ) %>%
map_dfr(~tibble(!!.x := double())) ->
col_order
DESeq_res_SIP_byTime_all_df_actino %>%
filter(Labelled == "Labelled") %>%
select(ASV, Site, Oxygen, Hours, log2FoldChange) %>%
pivot_wider(names_from = c(Site, Oxygen, Hours),
values_from = log2FoldChange,
names_sep = " ") %>%
bind_rows(col_order, .) %>%
# add_column(., !!!heatmap_blnk_df[setdiff(names(heatmap_blnk_df), names(.))])
# set_names() %>%
# select(c("ASV", "Certovo Oxic_12 h", "Certovo Oxic_24 h", "Certovo Oxic_48 h", "Certovo Oxic_72 h", "Certovo Anoxic_12 h", "Certovo Anoxic_24 h", "Certovo Anoxic_48 h", "Certovo Anoxic_216 h", "Plesne Oxic_12 h", "Plesne Oxic_24 h", "Plesne Oxic_48 h", "Plesne Oxic_72 h", "Plesne Anoxic_12 h", "Plesne Anoxic_24 h", "Plesne Anoxic_48 h", "Plesne Anoxic_216 h" )) %>%
# modify_if(is.double, ~replace_na(.x, "")) %>%
column_to_rownames("ASV") %>%
set_colnames(labels) ->
DESeq_res_SIP_byTime_all_df_actino_hm
gheatmap(
p = p_actino_t,
data = DESeq_res_SIP_byTime_all_df_actino_hm,
offset = 0, width = 1.6, font.size = 3,
low = "grey",
high = "darkred",
color = "grey95",
colnames_position = "bottom",
colnames_offset_x = -0.05,
colnames_offset_y = 1,
colnames_angle = 25,
hjust = 0,
legend_title = "L2FC") +
theme_tree(legend.position = "bottom") +
theme(
axis.text.x = element_markdown(color = "red", size = 11)
) +
guides(colour = guide_legend(nrow = 3,
byrow = TRUE))
p <- ggtree(Actino_ps) +
# geom_treescale() +
geom_tiplab(size=2) +
# geom_treescale(x=2008, y=1, offset=2)
gheatmap(p, DESeq_res_SIP_byTime_all_df_actino_hm) +
# scale_fill_brewer()
scale_fill_manual(breaks=c("Unlabelled", "Labelled"),
values=c("firebrick", "darkgreen"), name="DESeq_res_SIP_byTime_all_df_actino_hm")
gheatmap(p_actino_t,
DESeq_res_SIP_byTime_all_df_actino_hm, offset = 10, color = NULL,
colnames_position = "top",
colnames_angle = 90, colnames_offset_y = 1,
hjust = 0, font.size = 2)
+
scale_fill_manual(values = heatmap.colours, breaks=0:15)
library(ggnewscale)
# p_actino_t %<+% DESeq_res_SIP_byTime_all_df_actino# add data
# p_actino_t +
# new_scale_colour() +
# geom_fruit(
# data = DESeq_res_SIP_byTime_all_df_actino,
# geom = geom_point,
# mapping = aes(y = ASV, x = Site_oxygen, fill = Labelled),
# offset=0.08, # The distance between layers, default is 0.03 of x range of tree.
# pwidth=0.25, # width of the layer, default is 0.2 of x range of tree.
# axis.params=list(
# axis="x", # add axis text of the layer.
# text.angle=-45, # the text size of axis.
# hjust=0 # adust the horizontal position of text of axis.
# )
# )
beast_file <- system.file("examples/MCC_FluA_H3.tree", package="ggtree")
beast_tree <- read.beast(beast_file)
genotype_file <- system.file("examples/Genotype.txt", package="ggtree")
genotype <- read.table(genotype_file, sep="\t", stringsAsFactor=F)
colnames(genotype) <- sub("\\.$", "", colnames(genotype))
p <- ggtree(beast_tree, mrsd="2013-01-01") +
geom_treescale(x=2008, y=1, offset=2) +
geom_tiplab(size=2)
gheatmap(p, genotype, offset=5, width=0.5, font.size=3,
colnames_angle=-45, hjust=0) +
scale_fill_manual(breaks=c("HuH3N2", "pdm", "trig"),
values=c("steelblue", "firebrick", "darkgreen"), name="genotype") Current session info
─ Session info ─────────────────────────────────────────────────────────────────────────
setting value
version R version 4.0.3 (2020-10-10)
os Ubuntu 18.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Prague
date 2021-02-09
─ Packages ─────────────────────────────────────────────────────────────────────────────
package * version date lib
ade4 1.7-16 2020-10-28 [1]
annotate 1.66.0 2020-04-27 [1]
AnnotationDbi 1.50.3 2020-07-25 [1]
ape 5.4-1 2020-08-13 [1]
aplot 0.0.6 2020-09-03 [1]
ashr 2.2-47 2020-02-20 [1]
assertthat 0.2.1 2019-03-21 [1]
backports 1.2.1 2020-12-09 [1]
Biobase * 2.48.0 2020-04-27 [1]
BiocGenerics * 0.34.0 2020-04-27 [1]
BiocManager 1.30.10 2019-11-16 [1]
BiocParallel 1.22.0 2020-04-27 [1]
biomformat 1.16.0 2020-04-27 [1]
Biostrings * 2.56.0 2020-04-27 [1]
bit 4.0.4 2020-08-04 [1]
bit64 4.0.5 2020-08-30 [1]
bitops 1.0-6 2013-08-17 [1]
blob 1.2.1 2020-01-20 [1]
broom 0.7.4 2021-01-29 [1]
cachem 1.0.3 2021-02-04 [1]
cellranger 1.1.0 2016-07-27 [1]
cli 2.3.0 2021-01-31 [1]
clipr 0.7.1 2020-10-08 [1]
cluster 2.1.0 2019-06-19 [1]
codetools 0.2-18 2020-11-04 [1]
colorspace 2.0-0 2020-11-11 [1]
crayon 1.4.0 2021-01-30 [1]
data.table 1.13.6 2020-12-30 [1]
DBI 1.1.1 2021-01-15 [1]
dbplyr 2.1.0 2021-02-03 [1]
DelayedArray * 0.14.1 2020-07-14 [1]
desc 1.2.0 2018-05-01 [1]
DESeq2 * 1.28.1 2020-05-12 [1]
details 0.2.1 2020-01-12 [1]
digest 0.6.27 2020-10-24 [1]
dplyr * 1.0.4 2021-02-02 [1]
ellipsis 0.3.1 2020-05-15 [1]
evaluate 0.14 2019-05-28 [1]
extrafont * 0.17 2014-12-08 [1]
extrafontdb 1.0 2012-06-11 [1]
farver 2.0.3 2020-01-16 [1]
fastmap 1.1.0 2021-01-25 [1]
forcats * 0.5.1 2021-01-27 [1]
foreach 1.5.1 2020-10-15 [1]
fs 1.5.0 2020-07-31 [1]
gdtools 0.2.3 2021-01-06 [1]
genefilter 1.70.0 2020-04-27 [1]
geneplotter 1.66.0 2020-04-27 [1]
generics 0.1.0 2020-10-31 [1]
GenomeInfoDb * 1.24.2 2020-06-15 [1]
GenomeInfoDbData 1.2.3 2020-08-13 [1]
GenomicRanges * 1.40.0 2020-04-27 [1]
ggnewscale 0.4.5 2021-01-11 [1]
ggplot2 * 3.3.3 2020-12-30 [1]
ggpomological * 0.1.2 2020-08-13 [1]
ggrepel * 0.9.1 2021-01-15 [1]
ggsci 2.9 2018-05-14 [1]
ggtext * 0.1.1 2020-12-17 [1]
ggtree * 2.2.4 2020-07-28 [1]
ggtreeExtra * 1.1.4 2021-02-01 [1]
glue * 1.4.2 2020-08-27 [1]
gridExtra 2.3 2017-09-09 [1]
gridtext 0.1.4 2020-12-10 [1]
gtable 0.3.0 2019-03-25 [1]
haven 2.3.1 2020-06-01 [1]
highr 0.8 2019-03-20 [1]
hms 1.0.0 2021-01-13 [1]
htmltools 0.5.1.1 2021-01-22 [1]
HTSSIP * 1.4.1 2021-01-15 [1]
httr 1.4.2 2020-07-20 [1]
igraph 1.2.6 2020-10-06 [1]
invgamma 1.1 2017-05-07 [1]
IRanges * 2.22.2 2020-05-21 [1]
irlba 2.3.3 2019-02-05 [1]
iterators 1.0.13 2020-10-15 [1]
jsonlite 1.7.2 2020-12-09 [1]
kableExtra * 1.3.1 2020-10-22 [1]
knitr 1.31 2021-01-27 [1]
labeling 0.4.2 2020-10-20 [1]
lattice * 0.20-41 2020-04-02 [1]
lazyeval 0.2.2 2019-03-15 [1]
lifecycle 0.2.0 2020-03-06 [1]
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progress 1.2.2 2019-05-16 [1]
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R6 2.5.0 2020-10-28 [1]
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Rhdf5lib 1.10.1 2020-07-09 [1]
rlang 0.4.10 2020-12-30 [1]
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rstudioapi 0.13 2020-11-12 [1]
Rttf2pt1 1.3.8 2020-01-10 [1]
rvcheck 0.1.8 2020-03-01 [1]
rvest 0.3.6 2020-07-25 [1]
S4Vectors * 0.26.1 2020-05-16 [1]
scales * 1.1.1 2020-05-11 [1]
sessioninfo 1.1.1 2018-11-05 [1]
speedyseq * 0.5.3.9001 2020-10-27 [1]
SQUAREM 2021.1 2021-01-13 [1]
stringi 1.5.3 2020-09-09 [1]
stringr * 1.4.0 2019-02-10 [1]
SummarizedExperiment * 1.18.2 2020-07-09 [1]
survival 3.2-7 2020-09-28 [1]
svglite * 1.2.3.2 2020-07-07 [1]
systemfonts 1.0.0 2021-02-01 [1]
tibble * 3.0.6 2021-01-29 [1]
tidyr * 1.1.2 2020-08-27 [1]
tidyselect 1.1.0 2020-05-11 [1]
tidytree 0.3.3 2020-04-02 [1]
tidyverse * 1.3.0 2019-11-21 [1]
treeio 1.12.0 2020-04-27 [1]
truncnorm 1.0-8 2018-02-27 [1]
vctrs 0.3.6 2020-12-17 [1]
vegan * 2.5-7 2020-11-28 [1]
viridis * 0.5.1 2018-03-29 [1]
viridisLite * 0.3.0 2018-02-01 [1]
visdat * 0.6.0.9000 2021-02-01 [1]
webshot 0.5.2 2019-11-22 [1]
withr 2.4.1 2021-01-26 [1]
xfun 0.20 2021-01-06 [1]
XML 3.99-0.5 2020-07-23 [1]
xml2 1.3.2 2020-04-23 [1]
xtable 1.8-4 2019-04-21 [1]
XVector * 0.28.0 2020-04-27 [1]
yaml 2.2.1 2020-02-01 [1]
zlibbioc 1.34.0 2020-04-27 [1]
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[1] /home/angel/R/library
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library